Helicopter gearbox diagnostics and prognostics using vibration signature analysis

Rotocraft safety, survivability, and mission effectiveness depend on the structural integrity of dynamic components. The need exists to develop an on-board, continuous vibration diagnostic system to detect and to prognosticate faults in these components prior to failure. This paper overviews a generic fault detection, isolation, and estimation (FDIE) architecture for condition-based machinery maintenance applications. Neural network-based fault pattern recognition is used to analyze normal and defect vibration signatures in helicopter transmissions. Data from nine seeded-fault test-rig experiments, each corresponding to one of six different fault/no fault conditions, were used to train and evaluate polynomial neural networks at pattern classification tasks. Features were generated using the amplitude spectra of the time-series vibration signatures. The Algorithm for Synthesis of Polynomial Networks for Classification (CLASS), a neural network software package that utilizes a constrained, minimum-logistic-loss criterion for multiclass problem, was used to perform the pattern recognition tasks. By employing a multiple-look post-processing strategy, perfect vibration signature classification was achieved.